Abstract:
One of the major goals of current research in oncology is to understand the diversity of ways in which healthy cells can transform into increasingly aggressive cancerous tumors. By understanding the major sequences of genetic abnormalities by which tumors develop, it is hoped that we can better identify subgroups of patients who will benefit from similar treatments and help to design effective diagnostics and therapeutics for those subgroups. Algorithms from the field of phylogenetics, or the inference of evolutionary trees, have proven a powerful method for inferring likely pathways of tumor progression, but their use has been limited by our inability to precisely characterize the individual cell types that make up tumors due to the high heterogeneity from-cell-to-cell in individual tumors. Our group has developed a computational strategy for attempting to characterize well-populated cell types from genomic assays of tumors, which can then be used in phylogenetic inference of possible progression pathways. Our approach relies on a class of computational method called an “unmixing” algorithm, which uses techniques derived from computational geometry to explain expression microarray or comparative genomic hybridization (aCGH) data from many tumor profiles as different mixtures of a common set fundamental components. We interpret these components as RNA expression or DNA copy number profiles of discrete cell types commonly produced in tumor progression. We can then apply a statistical analysis to these inferred cell types to detect significantly aberrant genes or genomic regions that can serve as markers for phylogenetic inference. Finally, we apply tree reconstruction algorithms to identify likely evolutionary pathways among the cell types. These pathways identify possible unobserved ancestral cell types and suggest specific patterns of mutations that may characterize progression between those cell types. It is hoped that this approach to tumor phylogeny inference will prove valuable in identifying new clinically significant tumor, characterizing their causal mutations, and suggesting novel diagnostics or therapeutic targets.

Biography:
Dr. Schwartz is an Associate Professor at the Department of Biological Sciences at Carnegie Mellon University with additional appointments in the Carnegie Mellon University Lane Center for Computational Biology, Computer Science Department, and Machine Learning Department. He received a Ph.D. in Computer Science from MIT in June 2000 and subsequently did post-doctoral work in the MIT Department of Biology before moving to Celera Genomics to work on projects related to the sequencing of the human genome. He joined the Biological Sciences Department at Carnegie Mellon in 2002, where he works on various problems in modeling, simulation, and algorithms for biological analysis. His research currently predominantly focuses on stochastic models and associated algorithms for simulating large self-assembly systems and on algorithms for phylogenetics and genetic variation analysis. For his research accomplishments, he received a 2004 NSF CAREER Award and 2005 Presidential Early Career Award for Scientists and Engineers (PECASE). He is currently co-Director of the Carnegie Mellon/University of Pittsburgh Joint Ph.D. Program in Computational Biology, as well as one of the directors of Carnegie Mellon's B.S. and M.S. programs in Computational Biology. Prof. Schwartz has published over 60 research papers in the area of computational biology. He also wrote the textbook "Biological Modeling and Simulation," published by MIT Press, based on a class he developed at Carnegie Mellon.